In [1]:
%%capture
!pip install kagglehub
Download the file from kagglehub. it's about 2.78 GB¶
In [2]:
!pip install kagglehub
import kagglehub
# Download latest version
path = kagglehub.dataset_download("imsparsh/deam-mediaeval-dataset-emotional-analysis-in-music")
print("Path to dataset files:", path)
Requirement already satisfied: kagglehub in /usr/local/lib/python3.11/dist-packages (0.3.12) Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from kagglehub) (24.2) Requirement already satisfied: pyyaml in /usr/local/lib/python3.11/dist-packages (from kagglehub) (6.0.2) Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from kagglehub) (2.32.3) Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from kagglehub) (4.67.1) Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->kagglehub) (3.4.2) Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->kagglehub) (3.10) Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->kagglehub) (2.4.0) Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->kagglehub) (2025.4.26) Path to dataset files: /kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music
In [3]:
#just shows size of data
import os
def get_folder_size(start_path):
total_size = 0
for dirpath, dirnames, filenames in os.walk(start_path):
for f in filenames:
fp = os.path.join(dirpath, f)
# Skip if it’s a broken symlink
if os.path.isfile(fp):
total_size += os.path.getsize(fp)
return total_size
dataset_path = path
size_bytes = get_folder_size(dataset_path)
size_gb = size_bytes / (1024**3)
print(f" Total dataset size: {size_gb:.2f} GB")
Total dataset size: 2.78 GB
In [4]:
#lists all files downloaded
dataset_path = path
# List contents
for root, dirs, files in os.walk(dataset_path):
for file in files:
print(os.path.join(root, file))
Streaming output truncated to the last 5000 lines.
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/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/164.csv
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/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1485.csv
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/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/83.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/270.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1683.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/227.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1070.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1312.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1574.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1091.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1370.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/804.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1083.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1260.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1255.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1078.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1004.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/428.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/886.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1103.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1780.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1844.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/178.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1968.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1879.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1088.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/239.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/song_level/static_annotations_songs_2000_2058.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations per each rater/song_level/static_annotations_songs_1_2000.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations averaged per song/dynamic (per second annotations)/arousal.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations averaged per song/dynamic (per second annotations)/valence.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations averaged per song/song_level/static_annotations_averaged_songs_2000_2058.csv
/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations averaged per song/song_level/static_annotations_averaged_songs_1_2000.csv
In [5]:
for root, dirs, files in os.walk(dataset_path):
for file in files:
filepath = os.path.join(root, file)
size_mb = os.path.getsize(filepath) / (1024 * 1024)
print(f"{file:50} {size_mb:>8.2f} MB")
Streaming output truncated to the last 5000 lines.
740.csv 2.37 MB
164.csv 2.69 MB
697.csv 0.59 MB
1485.csv 0.30 MB
400.csv 1.48 MB
1157.csv 0.30 MB
19.csv 1.41 MB
1131.csv 0.30 MB
1085.csv 0.30 MB
1671.csv 0.30 MB
2052.csv 1.27 MB
1482.csv 0.30 MB
820.csv 1.63 MB
691.csv 2.46 MB
1349.csv 0.30 MB
73.csv 2.07 MB
1296.csv 0.30 MB
537.csv 1.96 MB
1315.csv 0.30 MB
656.csv 1.05 MB
1210.csv 0.30 MB
138.csv 2.92 MB
592.csv 2.09 MB
5.csv 1.09 MB
1090.csv 0.30 MB
1545.csv 0.30 MB
1853.csv 0.30 MB
464.csv 0.98 MB
1277.csv 0.30 MB
1105.csv 0.30 MB
702.csv 0.61 MB
556.csv 1.00 MB
184.csv 2.30 MB
216.csv 1.76 MB
1283.csv 0.30 MB
1154.csv 0.30 MB
1955.csv 0.30 MB
1452.csv 0.30 MB
1496.csv 0.30 MB
564.csv 1.02 MB
1648.csv 0.30 MB
202.csv 1.42 MB
1832.csv 0.29 MB
35.csv 1.79 MB
1515.csv 0.30 MB
1107.csv 0.30 MB
850.csv 1.38 MB
140.csv 1.85 MB
404.csv 1.80 MB
1820.csv 0.30 MB
657.csv 1.50 MB
1290.csv 0.30 MB
1833.csv 0.30 MB
1353.csv 0.30 MB
1308.csv 0.30 MB
1186.csv 0.30 MB
1517.csv 0.29 MB
422.csv 1.11 MB
1862.csv 0.30 MB
1423.csv 0.30 MB
1245.csv 0.30 MB
1172.csv 0.30 MB
115.csv 0.81 MB
1024.csv 0.29 MB
1095.csv 0.30 MB
1027.csv 0.30 MB
2.csv 1.47 MB
1916.csv 0.30 MB
2009.csv 1.54 MB
967.csv 1.15 MB
1256.csv 0.30 MB
488.csv 1.99 MB
41.csv 1.38 MB
1020.csv 0.30 MB
45.csv 1.78 MB
2050.csv 1.38 MB
131.csv 1.32 MB
1419.csv 0.30 MB
882.csv 1.17 MB
834.csv 1.58 MB
1036.csv 0.30 MB
1046.csv 0.30 MB
815.csv 1.61 MB
1736.csv 0.30 MB
1954.csv 0.29 MB
1992.csv 0.30 MB
144.csv 1.38 MB
1765.csv 0.30 MB
1585.csv 0.30 MB
1226.csv 0.30 MB
805.csv 1.25 MB
1634.csv 0.29 MB
1241.csv 0.30 MB
1330.csv 0.29 MB
236.csv 2.82 MB
1681.csv 0.30 MB
1220.csv 0.30 MB
242.csv 1.96 MB
799.csv 3.27 MB
1834.csv 0.30 MB
13.csv 1.12 MB
1978.csv 0.30 MB
1652.csv 0.30 MB
844.csv 1.22 MB
718.csv 1.83 MB
1806.csv 0.30 MB
43.csv 1.40 MB
631.csv 0.49 MB
1467.csv 0.30 MB
1483.csv 0.30 MB
431.csv 2.46 MB
2021.csv 2.06 MB
1928.csv 0.30 MB
112.csv 1.20 MB
1930.csv 0.29 MB
932.csv 1.37 MB
1487.csv 0.30 MB
1300.csv 0.30 MB
368.csv 0.88 MB
1079.csv 0.30 MB
1472.csv 0.30 MB
307.csv 0.44 MB
218.csv 0.89 MB
1240.csv 0.30 MB
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525.csv 1.39 MB
1567.csv 0.30 MB
325.csv 1.69 MB
1775.csv 0.30 MB
1732.csv 0.30 MB
1713.csv 0.30 MB
1281.csv 0.30 MB
639.csv 1.92 MB
1560.csv 0.30 MB
1958.csv 0.30 MB
1638.csv 0.30 MB
1625.csv 0.30 MB
146.csv 0.32 MB
515.csv 1.22 MB
536.csv 0.89 MB
977.csv 0.82 MB
274.csv 0.47 MB
574.csv 1.04 MB
463.csv 1.18 MB
1536.csv 0.30 MB
329.csv 1.51 MB
1104.csv 0.30 MB
1451.csv 0.30 MB
985.csv 2.32 MB
278.csv 1.00 MB
1119.csv 0.30 MB
1332.csv 0.30 MB
1697.csv 0.30 MB
370.csv 1.19 MB
2051.csv 1.70 MB
280.csv 1.46 MB
848.csv 1.52 MB
1341.csv 0.30 MB
1810.csv 0.30 MB
987.csv 0.98 MB
1794.csv 0.30 MB
560.csv 1.52 MB
1380.csv 0.30 MB
361.csv 0.63 MB
1602.csv 0.30 MB
1509.csv 0.30 MB
1197.csv 0.30 MB
1843.csv 0.30 MB
2017.csv 0.33 MB
484.csv 2.79 MB
2029.csv 1.12 MB
1473.csv 0.30 MB
1162.csv 0.30 MB
1092.csv 0.30 MB
1611.csv 0.30 MB
399.csv 1.44 MB
1462.csv 0.30 MB
707.csv 0.97 MB
462.csv 2.77 MB
866.csv 0.94 MB
1666.csv 0.30 MB
750.csv 3.27 MB
632.csv 2.11 MB
286.csv 1.01 MB
1659.csv 0.29 MB
1470.csv 0.30 MB
2040.csv 1.42 MB
747.csv 2.24 MB
181.csv 1.49 MB
2049.csv 1.85 MB
148.csv 2.53 MB
1770.csv 0.30 MB
330.csv 1.01 MB
190.csv 1.71 MB
1019.csv 0.30 MB
1381.csv 0.30 MB
1991.csv 0.30 MB
684.csv 1.94 MB
1094.csv 0.30 MB
1984.csv 0.30 MB
2006.csv 1.08 MB
721.csv 3.29 MB
52.csv 1.34 MB
456.csv 0.31 MB
1963.csv 0.30 MB
1188.csv 0.30 MB
1860.csv 0.30 MB
745.csv 1.98 MB
1417.csv 0.30 MB
737.csv 0.71 MB
2038.csv 1.58 MB
1223.csv 0.30 MB
872.csv 1.20 MB
856.csv 1.52 MB
1917.csv 0.30 MB
1422.csv 0.30 MB
634.csv 3.16 MB
941.csv 2.13 MB
1272.csv 0.30 MB
671.csv 1.88 MB
1747.csv 0.30 MB
640.csv 1.13 MB
1870.csv 0.30 MB
1859.csv 0.30 MB
1265.csv 0.30 MB
1650.csv 0.30 MB
957.csv 2.59 MB
1871.csv 0.30 MB
344.csv 0.68 MB
137.csv 0.64 MB
2003.csv 1.61 MB
469.csv 1.02 MB
1927.csv 0.30 MB
1425.csv 0.30 MB
416.csv 1.21 MB
1201.csv 0.30 MB
1995.csv 0.30 MB
364.csv 1.00 MB
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622.csv 1.04 MB
452.csv 1.95 MB
1289.csv 0.30 MB
1433.csv 0.30 MB
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4.csv 1.49 MB
1909.csv 0.30 MB
2036.csv 1.92 MB
301.csv 1.19 MB
795.csv 1.43 MB
478.csv 0.93 MB
212.csv 1.03 MB
1561.csv 0.30 MB
810.csv 1.30 MB
961.csv 2.14 MB
1618.csv 0.30 MB
143.csv 1.12 MB
1175.csv 0.30 MB
1596.csv 0.30 MB
1113.csv 0.30 MB
1623.csv 0.30 MB
968.csv 1.84 MB
154.csv 0.87 MB
1318.csv 0.30 MB
551.csv 1.04 MB
1408.csv 0.30 MB
1137.csv 0.30 MB
1892.csv 0.30 MB
405.csv 1.67 MB
625.csv 1.50 MB
1093.csv 0.30 MB
1051.csv 0.30 MB
1158.csv 0.30 MB
1880.csv 0.30 MB
1231.csv 0.30 MB
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1491.csv 0.30 MB
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855.csv 1.18 MB
1852.csv 0.30 MB
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1576.csv 0.30 MB
1469.csv 0.30 MB
1250.csv 0.30 MB
151.csv 1.08 MB
22.csv 1.49 MB
1054.csv 0.30 MB
558.csv 0.39 MB
1815.csv 0.30 MB
403.csv 1.78 MB
1167.csv 0.30 MB
949.csv 0.50 MB
401.csv 1.54 MB
811.csv 1.63 MB
1400.csv 0.30 MB
1145.csv 0.30 MB
738.csv 2.28 MB
1633.csv 0.30 MB
1873.csv 0.30 MB
1181.csv 0.30 MB
942.csv 1.21 MB
1757.csv 0.30 MB
1495.csv 0.30 MB
1830.csv 0.30 MB
1781.csv 0.30 MB
1552.csv 0.30 MB
725.csv 0.89 MB
1184.csv 0.29 MB
1694.csv 0.30 MB
297.csv 2.46 MB
2024.csv 2.64 MB
1192.csv 0.30 MB
854.csv 1.08 MB
440.csv 1.47 MB
1194.csv 0.30 MB
1933.csv 0.30 MB
1786.csv 0.30 MB
170.csv 0.89 MB
1649.csv 0.30 MB
1759.csv 0.30 MB
497.csv 2.47 MB
1030.csv 0.30 MB
1798.csv 0.30 MB
372.csv 1.34 MB
128.csv 1.14 MB
1546.csv 0.29 MB
1823.csv 0.30 MB
1872.csv 0.30 MB
1962.csv 0.30 MB
179.csv 0.95 MB
1356.csv 0.29 MB
1640.csv 0.29 MB
1553.csv 0.30 MB
1607.csv 0.30 MB
160.csv 1.03 MB
1072.csv 0.30 MB
1564.csv 0.30 MB
96.csv 1.07 MB
1285.csv 0.30 MB
1480.csv 0.30 MB
1857.csv 0.30 MB
1748.csv 0.30 MB
290.csv 2.50 MB
67.csv 1.48 MB
390.csv 0.96 MB
80.csv 1.58 MB
40.csv 1.93 MB
109.csv 0.97 MB
410.csv 2.20 MB
1573.csv 0.30 MB
960.csv 1.14 MB
228.csv 0.75 MB
1856.csv 0.29 MB
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1338.csv 0.30 MB
781.csv 1.66 MB
334.csv 1.59 MB
1587.csv 0.30 MB
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448.csv 1.08 MB
341.csv 0.97 MB
306.csv 1.14 MB
1390.csv 0.30 MB
44.csv 1.66 MB
582.csv 1.00 MB
1590.csv 0.30 MB
492.csv 1.98 MB
1449.csv 0.30 MB
1147.csv 0.30 MB
158.csv 0.59 MB
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2019.csv 1.18 MB
1309.csv 0.29 MB
1097.csv 0.30 MB
1890.csv 0.30 MB
715.csv 2.39 MB
824.csv 0.96 MB
1906.csv 0.30 MB
2025.csv 2.94 MB
1656.csv 0.30 MB
1941.csv 0.30 MB
1206.csv 0.30 MB
513.csv 0.80 MB
1189.csv 0.30 MB
1519.csv 0.30 MB
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1657.csv 0.30 MB
775.csv 1.84 MB
610.csv 1.57 MB
1918.csv 0.30 MB
726.csv 1.40 MB
1426.csv 0.30 MB
355.csv 0.85 MB
1411.csv 0.30 MB
1269.csv 0.30 MB
1668.csv 0.30 MB
845.csv 1.10 MB
1397.csv 0.30 MB
1783.csv 0.30 MB
1207.csv 0.30 MB
1438.csv 0.30 MB
646.csv 1.48 MB
430.csv 1.20 MB
1606.csv 0.30 MB
1150.csv 0.30 MB
614.csv 1.50 MB
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1303.csv 0.30 MB
729.csv 1.47 MB
2026.csv 3.51 MB
445.csv 0.33 MB
512.csv 1.44 MB
1822.csv 0.30 MB
609.csv 2.03 MB
1855.csv 0.30 MB
86.csv 1.76 MB
1121.csv 0.30 MB
87.csv 0.96 MB
787.csv 1.15 MB
1362.csv 0.30 MB
1965.csv 0.30 MB
1670.csv 0.30 MB
2000.csv 0.30 MB
338.csv 1.72 MB
1716.csv 0.30 MB
219.csv 1.61 MB
156.csv 1.83 MB
1108.csv 0.30 MB
2046.csv 1.85 MB
1448.csv 0.30 MB
1902.csv 0.30 MB
1808.csv 0.30 MB
85.csv 1.67 MB
1003.csv 0.30 MB
973.csv 1.55 MB
8.csv 0.78 MB
58.csv 0.51 MB
1354.csv 0.30 MB
1695.csv 0.30 MB
1969.csv 0.30 MB
1444.csv 0.30 MB
1889.csv 0.29 MB
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12.csv 0.82 MB
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863.csv 1.56 MB
380.csv 1.89 MB
152.csv 0.77 MB
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1957.csv 0.30 MB
1122.csv 0.30 MB
395.csv 2.12 MB
1848.csv 0.30 MB
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1156.csv 0.30 MB
50.csv 1.49 MB
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696.csv 1.85 MB
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1320.csv 0.30 MB
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299.csv 1.09 MB
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1985.csv 0.30 MB
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39.csv 1.22 MB
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118.csv 1.87 MB
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62.csv 1.00 MB
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535.csv 1.87 MB
2047.csv 1.90 MB
237.csv 1.85 MB
1998.csv 0.30 MB
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1981.csv 0.30 MB
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234.csv 2.88 MB
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88.csv 1.50 MB
703.csv 2.73 MB
289.csv 1.52 MB
1580.csv 0.30 MB
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1268.csv 0.30 MB
621.csv 2.79 MB
468.csv 2.73 MB
1151.csv 0.30 MB
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79.csv 2.51 MB
1233.csv 0.30 MB
1424.csv 0.30 MB
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1547.csv 0.30 MB
649.csv 2.54 MB
899.csv 2.66 MB
742.csv 0.48 MB
1387.csv 0.30 MB
1876.csv 0.30 MB
790.csv 1.12 MB
1275.csv 0.30 MB
2020.csv 1.25 MB
668.csv 1.14 MB
427.csv 1.11 MB
597.csv 0.93 MB
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1967.csv 0.30 MB
149.csv 2.25 MB
129.csv 0.91 MB
353.csv 1.14 MB
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897.csv 1.92 MB
485.csv 1.99 MB
480.csv 2.90 MB
335.csv 1.31 MB
561.csv 1.53 MB
990.csv 1.56 MB
1291.csv 0.30 MB
300.csv 1.88 MB
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1202.csv 0.30 MB
2034.csv 0.73 MB
1997.csv 0.30 MB
324.csv 0.72 MB
371.csv 1.14 MB
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607.csv 3.67 MB
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321.csv 2.01 MB
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833.csv 2.35 MB
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107.csv 1.33 MB
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262.csv 1.02 MB
520.csv 0.97 MB
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1173.csv 0.30 MB
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136.csv 2.43 MB
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54.csv 1.52 MB
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1990.csv 0.30 MB
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2023.csv 1.56 MB
247.csv 0.81 MB
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In [6]:
%%capture
!pip install librosa
In [7]:
import librosa
import glob
audio_dir = os.path.join(dataset_path, "DEAM_audio", "MEMD_audio")
mp3_files = glob.glob(os.path.join(audio_dir, "*.mp3"))
print(f"Found {len(mp3_files)} MP3 files")
if mp3_files:
audio_file = mp3_files[0]
y, sr = librosa.load(audio_file)
print("🎧 Sample rate:", sr)
print("🎵 Duration (seconds):", len(y) / sr)
print("📐 Shape:", y.shape)
print("📂 File path:", audio_file)
Found 1802 MP3 files 🎧 Sample rate: 22050 🎵 Duration (seconds): 45.000090702947844 📐 Shape: (992252,) 📂 File path: /kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_audio/MEMD_audio/1024.mp3
In [8]:
csv_files = glob.glob(os.path.join(dataset_path, "**/*.csv"), recursive=True)
print(csv_files)
['/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/860.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/142.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/347.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1523.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/2011.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1253.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1329.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1946.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1252.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/758.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1531.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1319.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/233.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1921.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/826.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/108.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1163.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1730.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1058.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1166.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/608.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/18.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/198.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1313.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/606.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1110.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1453.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1849.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1888.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1219.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1715.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1953.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/2041.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/986.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/794.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1982.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/910.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1135.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1421.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1398.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1134.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1185.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/350.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1734.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1532.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1887.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1581.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1176.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/482.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/231.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/191.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/92.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/858.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1655.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1714.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1577.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1246.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/777.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/629.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/187.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1818.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1069.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/298.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1750.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/229.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/429.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1914.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1102.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1653.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1753.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1688.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/425.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/391.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/650.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1364.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/645.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1475.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/20.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1624.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/42.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/763.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1196.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/90.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/779.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/660.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1936.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/402.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1009.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1503.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/2008.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/857.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/318.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1297.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/2037.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/311.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/2013.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1907.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1431.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/71.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1359.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1040.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1178.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/449.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/207.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/620.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1474.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/577.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/830.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/313.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/667.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1379.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/159.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1302.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/310.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/851.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/383.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1209.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/55.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1813.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/162.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1582.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1701.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1717.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1541.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/279.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1164.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1785.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1773.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/303.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/284.csv', 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'/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1721.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1628.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/487.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/314.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1179.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1829.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1218.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/903.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1615.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1010.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1865.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1805.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/161.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/891.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1706.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/999.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/666.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1443.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/419.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1539.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1831.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1000.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/530.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1080.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/938.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/24.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1011.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1579.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/78.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1864.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1575.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/235.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1305.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/585.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/479.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1142.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/723.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1259.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1837.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1441.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/461.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1348.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1608.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/209.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1437.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/163.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1947.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1038.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1310.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/features/features/1746.csv', 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'/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations averaged per song/dynamic (per second annotations)/valence.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations averaged per song/song_level/static_annotations_averaged_songs_2000_2058.csv', '/kaggle/input/deam-mediaeval-dataset-emotional-analysis-in-music/DEAM_Annotations/annotations/annotations averaged per song/song_level/static_annotations_averaged_songs_1_2000.csv']
In [9]:
import pandas as pd
df = pd.read_csv(csv_files[0])
print("🧾 Shape:", df.shape)
print("📋 Columns:", df.columns)
print(df.head())
🧾 Shape: (335, 1) 📋 Columns: Index(['frameTime;F0final_sma_stddev;F0final_sma_amean;voicingFinalUnclipped_sma_stddev;voicingFinalUnclipped_sma_amean;jitterLocal_sma_stddev;jitterLocal_sma_amean;jitterDDP_sma_stddev;jitterDDP_sma_amean;shimmerLocal_sma_stddev;shimmerLocal_sma_amean;logHNR_sma_stddev;logHNR_sma_amean;audspec_lengthL1norm_sma_stddev;audspec_lengthL1norm_sma_amean;audspecRasta_lengthL1norm_sma_stddev;audspecRasta_lengthL1norm_sma_amean;pcm_RMSenergy_sma_stddev;pcm_RMSenergy_sma_amean;pcm_zcr_sma_stddev;pcm_zcr_sma_amean;audSpec_Rfilt_sma[0]_stddev;audSpec_Rfilt_sma[0]_amean;audSpec_Rfilt_sma[1]_stddev;audSpec_Rfilt_sma[1]_amean;audSpec_Rfilt_sma[2]_stddev;audSpec_Rfilt_sma[2]_amean;audSpec_Rfilt_sma[3]_stddev;audSpec_Rfilt_sma[3]_amean;audSpec_Rfilt_sma[4]_stddev;audSpec_Rfilt_sma[4]_amean;audSpec_Rfilt_sma[5]_stddev;audSpec_Rfilt_sma[5]_amean;audSpec_Rfilt_sma[6]_stddev;audSpec_Rfilt_sma[6]_amean;audSpec_Rfilt_sma[7]_stddev;audSpec_Rfilt_sma[7]_amean;audSpec_Rfilt_sma[8]_stddev;audSpec_Rfilt_sma[8]_amean;audSpec_Rfilt_sma[9]_stddev;audSpec_Rfilt_sma[9]_amean;audSpec_Rfilt_sma[10]_stddev;audSpec_Rfilt_sma[10]_amean;audSpec_Rfilt_sma[11]_stddev;audSpec_Rfilt_sma[11]_amean;audSpec_Rfilt_sma[12]_stddev;audSpec_Rfilt_sma[12]_amean;audSpec_Rfilt_sma[13]_stddev;audSpec_Rfilt_sma[13]_amean;audSpec_Rfilt_sma[14]_stddev;audSpec_Rfilt_sma[14]_amean;audSpec_Rfilt_sma[15]_stddev;audSpec_Rfilt_sma[15]_amean;audSpec_Rfilt_sma[16]_stddev;audSpec_Rfilt_sma[16]_amean;audSpec_Rfilt_sma[17]_stddev;audSpec_Rfilt_sma[17]_amean;audSpec_Rfilt_sma[18]_stddev;audSpec_Rfilt_sma[18]_amean;audSpec_Rfilt_sma[19]_stddev;audSpec_Rfilt_sma[19]_amean;audSpec_Rfilt_sma[20]_stddev;audSpec_Rfilt_sma[20]_amean;audSpec_Rfilt_sma[21]_stddev;audSpec_Rfilt_sma[21]_amean;audSpec_Rfilt_sma[22]_stddev;audSpec_Rfilt_sma[22]_amean;audSpec_Rfilt_sma[23]_stddev;audSpec_Rfilt_sma[23]_amean;audSpec_Rfilt_sma[24]_stddev;audSpec_Rfilt_sma[24]_amean;audSpec_Rfilt_sma[25]_stddev;audSpec_Rfilt_sma[25]_amean;pcm_fftMag_fband250-650_sma_stddev;pcm_fftMag_fband250-650_sma_amean;pcm_fftMag_fband1000-4000_sma_stddev;pcm_fftMag_fband1000-4000_sma_amean;pcm_fftMag_spectralRollOff25.0_sma_stddev;pcm_fftMag_spectralRollOff25.0_sma_amean;pcm_fftMag_spectralRollOff50.0_sma_stddev;pcm_fftMag_spectralRollOff50.0_sma_amean;pcm_fftMag_spectralRollOff75.0_sma_stddev;pcm_fftMag_spectralRollOff75.0_sma_amean;pcm_fftMag_spectralRollOff90.0_sma_stddev;pcm_fftMag_spectralRollOff90.0_sma_amean;pcm_fftMag_spectralFlux_sma_stddev;pcm_fftMag_spectralFlux_sma_amean;pcm_fftMag_spectralCentroid_sma_stddev;pcm_fftMag_spectralCentroid_sma_amean;pcm_fftMag_spectralEntropy_sma_stddev;pcm_fftMag_spectralEntropy_sma_amean;pcm_fftMag_spectralVariance_sma_stddev;pcm_fftMag_spectralVariance_sma_amean;pcm_fftMag_spectralSkewness_sma_stddev;pcm_fftMag_spectralSkewness_sma_amean;pcm_fftMag_spectralKurtosis_sma_stddev;pcm_fftMag_spectralKurtosis_sma_amean;pcm_fftMag_spectralSlope_sma_stddev;pcm_fftMag_spectralSlope_sma_amean;pcm_fftMag_psySharpness_sma_stddev;pcm_fftMag_psySharpness_sma_amean;pcm_fftMag_spectralHarmonicity_sma_stddev;pcm_fftMag_spectralHarmonicity_sma_amean;pcm_fftMag_mfcc_sma[1]_stddev;pcm_fftMag_mfcc_sma[1]_amean;pcm_fftMag_mfcc_sma[2]_stddev;pcm_fftMag_mfcc_sma[2]_amean;pcm_fftMag_mfcc_sma[3]_stddev;pcm_fftMag_mfcc_sma[3]_amean;pcm_fftMag_mfcc_sma[4]_stddev;pcm_fftMag_mfcc_sma[4]_amean;pcm_fftMag_mfcc_sma[5]_stddev;pcm_fftMag_mfcc_sma[5]_amean;pcm_fftMag_mfcc_sma[6]_stddev;pcm_fftMag_mfcc_sma[6]_amean;pcm_fftMag_mfcc_sma[7]_stddev;pcm_fftMag_mfcc_sma[7]_amean;pcm_fftMag_mfcc_sma[8]_stddev;pcm_fftMag_mfcc_sma[8]_amean;pcm_fftMag_mfcc_sma[9]_stddev;pcm_fftMag_mfcc_sma[9]_amean;pcm_fftMag_mfcc_sma[10]_stddev;pcm_fftMag_mfcc_sma[10]_amean;pcm_fftMag_mfcc_sma[11]_stddev;pcm_fftMag_mfcc_sma[11]_amean;pcm_fftMag_mfcc_sma[12]_stddev;pcm_fftMag_mfcc_sma[12]_amean;pcm_fftMag_mfcc_sma[13]_stddev;pcm_fftMag_mfcc_sma[13]_amean;pcm_fftMag_mfcc_sma[14]_stddev;pcm_fftMag_mfcc_sma[14]_amean;F0final_sma_de_stddev;F0final_sma_de_amean;voicingFinalUnclipped_sma_de_stddev;voicingFinalUnclipped_sma_de_amean;jitterLocal_sma_de_stddev;jitterLocal_sma_de_amean;jitterDDP_sma_de_stddev;jitterDDP_sma_de_amean;shimmerLocal_sma_de_stddev;shimmerLocal_sma_de_amean;logHNR_sma_de_stddev;logHNR_sma_de_amean;audspec_lengthL1norm_sma_de_stddev;audspec_lengthL1norm_sma_de_amean;audspecRasta_lengthL1norm_sma_de_stddev;audspecRasta_lengthL1norm_sma_de_amean;pcm_RMSenergy_sma_de_stddev;pcm_RMSenergy_sma_de_amean;pcm_zcr_sma_de_stddev;pcm_zcr_sma_de_amean;audSpec_Rfilt_sma_de[0]_stddev;audSpec_Rfilt_sma_de[0]_amean;audSpec_Rfilt_sma_de[1]_stddev;audSpec_Rfilt_sma_de[1]_amean;audSpec_Rfilt_sma_de[2]_stddev;audSpec_Rfilt_sma_de[2]_amean;audSpec_Rfilt_sma_de[3]_stddev;audSpec_Rfilt_sma_de[3]_amean;audSpec_Rfilt_sma_de[4]_stddev;audSpec_Rfilt_sma_de[4]_amean;audSpec_Rfilt_sma_de[5]_stddev;audSpec_Rfilt_sma_de[5]_amean;audSpec_Rfilt_sma_de[6]_stddev;audSpec_Rfilt_sma_de[6]_amean;audSpec_Rfilt_sma_de[7]_stddev;audSpec_Rfilt_sma_de[7]_amean;audSpec_Rfilt_sma_de[8]_stddev;audSpec_Rfilt_sma_de[8]_amean;audSpec_Rfilt_sma_de[9]_stddev;audSpec_Rfilt_sma_de[9]_amean;audSpec_Rfilt_sma_de[10]_stddev;audSpec_Rfilt_sma_de[10]_amean;audSpec_Rfilt_sma_de[11]_stddev;audSpec_Rfilt_sma_de[11]_amean;audSpec_Rfilt_sma_de[12]_stddev;audSpec_Rfilt_sma_de[12]_amean;audSpec_Rfilt_sma_de[13]_stddev;audSpec_Rfilt_sma_de[13]_amean;audSpec_Rfilt_sma_de[14]_stddev;audSpec_Rfilt_sma_de[14]_amean;audSpec_Rfilt_sma_de[15]_stddev;audSpec_Rfilt_sma_de[15]_amean;audSpec_Rfilt_sma_de[16]_stddev;audSpec_Rfilt_sma_de[16]_amean;audSpec_Rfilt_sma_de[17]_stddev;audSpec_Rfilt_sma_de[17]_amean;audSpec_Rfilt_sma_de[18]_stddev;audSpec_Rfilt_sma_de[18]_amean;audSpec_Rfilt_sma_de[19]_stddev;audSpec_Rfilt_sma_de[19]_amean;audSpec_Rfilt_sma_de[20]_stddev;audSpec_Rfilt_sma_de[20]_amean;audSpec_Rfilt_sma_de[21]_stddev;audSpec_Rfilt_sma_de[21]_amean;audSpec_Rfilt_sma_de[22]_stddev;audSpec_Rfilt_sma_de[22]_amean;audSpec_Rfilt_sma_de[23]_stddev;audSpec_Rfilt_sma_de[23]_amean;audSpec_Rfilt_sma_de[24]_stddev;audSpec_Rfilt_sma_de[24]_amean;audSpec_Rfilt_sma_de[25]_stddev;audSpec_Rfilt_sma_de[25]_amean;pcm_fftMag_fband250-650_sma_de_stddev;pcm_fftMag_fband250-650_sma_de_amean;pcm_fftMag_fband1000-4000_sma_de_stddev;pcm_fftMag_fband1000-4000_sma_de_amean;pcm_fftMag_spectralRollOff25.0_sma_de_stddev;pcm_fftMag_spectralRollOff25.0_sma_de_amean;pcm_fftMag_spectralRollOff50.0_sma_de_stddev;pcm_fftMag_spectralRollOff50.0_sma_de_amean;pcm_fftMag_spectralRollOff75.0_sma_de_stddev;pcm_fftMag_spectralRollOff75.0_sma_de_amean;pcm_fftMag_spectralRollOff90.0_sma_de_stddev;pcm_fftMag_spectralRollOff90.0_sma_de_amean;pcm_fftMag_spectralFlux_sma_de_stddev;pcm_fftMag_spectralFlux_sma_de_amean;pcm_fftMag_spectralCentroid_sma_de_stddev;pcm_fftMag_spectralCentroid_sma_de_amean;pcm_fftMag_spectralEntropy_sma_de_stddev;pcm_fftMag_spectralEntropy_sma_de_amean;pcm_fftMag_spectralVariance_sma_de_stddev;pcm_fftMag_spectralVariance_sma_de_amean;pcm_fftMag_spectralSkewness_sma_de_stddev;pcm_fftMag_spectralSkewness_sma_de_amean;pcm_fftMag_spectralKurtosis_sma_de_stddev;pcm_fftMag_spectralKurtosis_sma_de_amean;pcm_fftMag_spectralSlope_sma_de_stddev;pcm_fftMag_spectralSlope_sma_de_amean;pcm_fftMag_psySharpness_sma_de_stddev;pcm_fftMag_psySharpness_sma_de_amean;pcm_fftMag_spectralHarmonicity_sma_de_stddev;pcm_fftMag_spectralHarmonicity_sma_de_amean;pcm_fftMag_mfcc_sma_de[1]_stddev;pcm_fftMag_mfcc_sma_de[1]_amean;pcm_fftMag_mfcc_sma_de[2]_stddev;pcm_fftMag_mfcc_sma_de[2]_amean;pcm_fftMag_mfcc_sma_de[3]_stddev;pcm_fftMag_mfcc_sma_de[3]_amean;pcm_fftMag_mfcc_sma_de[4]_stddev;pcm_fftMag_mfcc_sma_de[4]_amean;pcm_fftMag_mfcc_sma_de[5]_stddev;pcm_fftMag_mfcc_sma_de[5]_amean;pcm_fftMag_mfcc_sma_de[6]_stddev;pcm_fftMag_mfcc_sma_de[6]_amean;pcm_fftMag_mfcc_sma_de[7]_stddev;pcm_fftMag_mfcc_sma_de[7]_amean;pcm_fftMag_mfcc_sma_de[8]_stddev;pcm_fftMag_mfcc_sma_de[8]_amean;pcm_fftMag_mfcc_sma_de[9]_stddev;pcm_fftMag_mfcc_sma_de[9]_amean;pcm_fftMag_mfcc_sma_de[10]_stddev;pcm_fftMag_mfcc_sma_de[10]_amean;pcm_fftMag_mfcc_sma_de[11]_stddev;pcm_fftMag_mfcc_sma_de[11]_amean;pcm_fftMag_mfcc_sma_de[12]_stddev;pcm_fftMag_mfcc_sma_de[12]_amean;pcm_fftMag_mfcc_sma_de[13]_stddev;pcm_fftMag_mfcc_sma_de[13]_amean;pcm_fftMag_mfcc_sma_de[14]_stddev;pcm_fftMag_mfcc_sma_de[14]_amean'], dtype='object') frameTime;F0final_sma_stddev;F0final_sma_amean;voicingFinalUnclipped_sma_stddev;voicingFinalUnclipped_sma_amean;jitterLocal_sma_stddev;jitterLocal_sma_amean;jitterDDP_sma_stddev;jitterDDP_sma_amean;shimmerLocal_sma_stddev;shimmerLocal_sma_amean;logHNR_sma_stddev;logHNR_sma_amean;audspec_lengthL1norm_sma_stddev;audspec_lengthL1norm_sma_amean;audspecRasta_lengthL1norm_sma_stddev;audspecRasta_lengthL1norm_sma_amean;pcm_RMSenergy_sma_stddev;pcm_RMSenergy_sma_amean;pcm_zcr_sma_stddev;pcm_zcr_sma_amean;audSpec_Rfilt_sma[0]_stddev;audSpec_Rfilt_sma[0]_amean;audSpec_Rfilt_sma[1]_stddev;audSpec_Rfilt_sma[1]_amean;audSpec_Rfilt_sma[2]_stddev;audSpec_Rfilt_sma[2]_amean;audSpec_Rfilt_sma[3]_stddev;audSpec_Rfilt_sma[3]_amean;audSpec_Rfilt_sma[4]_stddev;audSpec_Rfilt_sma[4]_amean;audSpec_Rfilt_sma[5]_stddev;audSpec_Rfilt_sma[5]_amean;audSpec_Rfilt_sma[6]_stddev;audSpec_Rfilt_sma[6]_amean;audSpec_Rfilt_sma[7]_stddev;audSpec_Rfilt_sma[7]_amean;audSpec_Rfilt_sma[8]_stddev;audSpec_Rfilt_sma[8]_amean;audSpec_Rfilt_sma[9]_stddev;audSpec_Rfilt_sma[9]_amean;audSpec_Rfilt_sma[10]_stddev;audSpec_Rfilt_sma[10]_amean;audSpec_Rfilt_sma[11]_stddev;audSpec_Rfilt_sma[11]_amean;audSpec_Rfilt_sma[12]_stddev;audSpec_Rfilt_sma[12]_amean;audSpec_Rfilt_sma[13]_stddev;audSpec_Rfilt_sma[13]_amean;audSpec_Rfilt_sma[14]_stddev;audSpec_Rfilt_sma[14]_amean;audSpec_Rfilt_sma[15]_stddev;audSpec_Rfilt_sma[15]_amean;audSpec_Rfilt_sma[16]_stddev;audSpec_Rfilt_sma[16]_amean;audSpec_Rfilt_sma[17]_stddev;audSpec_Rfilt_sma[17]_amean;audSpec_Rfilt_sma[18]_stddev;audSpec_Rfilt_sma[18]_amean;audSpec_Rfilt_sma[19]_stddev;audSpec_Rfilt_sma[19]_amean;audSpec_Rfilt_sma[20]_stddev;audSpec_Rfilt_sma[20]_amean;audSpec_Rfilt_sma[21]_stddev;audSpec_Rfilt_sma[21]_amean;audSpec_Rfilt_sma[22]_stddev;audSpec_Rfilt_sma[22]_amean;audSpec_Rfilt_sma[23]_stddev;audSpec_Rfilt_sma[23]_amean;audSpec_Rfilt_sma[24]_stddev;audSpec_Rfilt_sma[24]_amean;audSpec_Rfilt_sma[25]_stddev;audSpec_Rfilt_sma[25]_amean;pcm_fftMag_fband250-650_sma_stddev;pcm_fftMag_fband250-650_sma_amean;pcm_fftMag_fband1000-4000_sma_stddev;pcm_fftMag_fband1000-4000_sma_amean;pcm_fftMag_spectralRollOff25.0_sma_stddev;pcm_fftMag_spectralRollOff25.0_sma_amean;pcm_fftMag_spectralRollOff50.0_sma_stddev;pcm_fftMag_spectralRollOff50.0_sma_amean;pcm_fftMag_spectralRollOff75.0_sma_stddev;pcm_fftMag_spectralRollOff75.0_sma_amean;pcm_fftMag_spectralRollOff90.0_sma_stddev;pcm_fftMag_spectralRollOff90.0_sma_amean;pcm_fftMag_spectralFlux_sma_stddev;pcm_fftMag_spectralFlux_sma_amean;pcm_fftMag_spectralCentroid_sma_stddev;pcm_fftMag_spectralCentroid_sma_amean;pcm_fftMag_spectralEntropy_sma_stddev;pcm_fftMag_spectralEntropy_sma_amean;pcm_fftMag_spectralVariance_sma_stddev;pcm_fftMag_spectralVariance_sma_amean;pcm_fftMag_spectralSkewness_sma_stddev;pcm_fftMag_spectralSkewness_sma_amean;pcm_fftMag_spectralKurtosis_sma_stddev;pcm_fftMag_spectralKurtosis_sma_amean;pcm_fftMag_spectralSlope_sma_stddev;pcm_fftMag_spectralSlope_sma_amean;pcm_fftMag_psySharpness_sma_stddev;pcm_fftMag_psySharpness_sma_amean;pcm_fftMag_spectralHarmonicity_sma_stddev;pcm_fftMag_spectralHarmonicity_sma_amean;pcm_fftMag_mfcc_sma[1]_stddev;pcm_fftMag_mfcc_sma[1]_amean;pcm_fftMag_mfcc_sma[2]_stddev;pcm_fftMag_mfcc_sma[2]_amean;pcm_fftMag_mfcc_sma[3]_stddev;pcm_fftMag_mfcc_sma[3]_amean;pcm_fftMag_mfcc_sma[4]_stddev;pcm_fftMag_mfcc_sma[4]_amean;pcm_fftMag_mfcc_sma[5]_stddev;pcm_fftMag_mfcc_sma[5]_amean;pcm_fftMag_mfcc_sma[6]_stddev;pcm_fftMag_mfcc_sma[6]_amean;pcm_fftMag_mfcc_sma[7]_stddev;pcm_fftMag_mfcc_sma[7]_amean;pcm_fftMag_mfcc_sma[8]_stddev;pcm_fftMag_mfcc_sma[8]_amean;pcm_fftMag_mfcc_sma[9]_stddev;pcm_fftMag_mfcc_sma[9]_amean;pcm_fftMag_mfcc_sma[10]_stddev;pcm_fftMag_mfcc_sma[10]_amean;pcm_fftMag_mfcc_sma[11]_stddev;pcm_fftMag_mfcc_sma[11]_amean;pcm_fftMag_mfcc_sma[12]_stddev;pcm_fftMag_mfcc_sma[12]_amean;pcm_fftMag_mfcc_sma[13]_stddev;pcm_fftMag_mfcc_sma[13]_amean;pcm_fftMag_mfcc_sma[14]_stddev;pcm_fftMag_mfcc_sma[14]_amean;F0final_sma_de_stddev;F0final_sma_de_amean;voicingFinalUnclipped_sma_de_stddev;voicingFinalUnclipped_sma_de_amean;jitterLocal_sma_de_stddev;jitterLocal_sma_de_amean;jitterDDP_sma_de_stddev;jitterDDP_sma_de_amean;shimmerLocal_sma_de_stddev;shimmerLocal_sma_de_amean;logHNR_sma_de_stddev;logHNR_sma_de_amean;audspec_lengthL1norm_sma_de_stddev;audspec_lengthL1norm_sma_de_amean;audspecRasta_lengthL1norm_sma_de_stddev;audspecRasta_lengthL1norm_sma_de_amean;pcm_RMSenergy_sma_de_stddev;pcm_RMSenergy_sma_de_amean;pcm_zcr_sma_de_stddev;pcm_zcr_sma_de_amean;audSpec_Rfilt_sma_de[0]_stddev;audSpec_Rfilt_sma_de[0]_amean;audSpec_Rfilt_sma_de[1]_stddev;audSpec_Rfilt_sma_de[1]_amean;audSpec_Rfilt_sma_de[2]_stddev;audSpec_Rfilt_sma_de[2]_amean;audSpec_Rfilt_sma_de[3]_stddev;audSpec_Rfilt_sma_de[3]_amean;audSpec_Rfilt_sma_de[4]_stddev;audSpec_Rfilt_sma_de[4]_amean;audSpec_Rfilt_sma_de[5]_stddev;audSpec_Rfilt_sma_de[5]_amean;audSpec_Rfilt_sma_de[6]_stddev;audSpec_Rfilt_sma_de[6]_amean;audSpec_Rfilt_sma_de[7]_stddev;audSpec_Rfilt_sma_de[7]_amean;audSpec_Rfi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0 0.000000;1.829557e+02;2.535646e+02;1.135925e-0... 1 0.500000;1.720156e+02;2.578612e+02;3.403050e-0... 2 1.000000;1.546783e+02;2.539422e+02;3.325405e-0... 3 1.500000;1.277238e+02;2.903989e+02;3.368178e-0... 4 2.000000;1.438418e+02;2.210409e+02;3.451350e-0...
Below we look at the Arousal, Valence and Static annotations files.¶
In [10]:
valence_path = os.path.join(dataset_path, "DEAM_Annotations", "annotations", "annotations averaged per song", "dynamic (per second annotations)", "valence.csv")
arousal_path = os.path.join(dataset_path, "DEAM_Annotations", "annotations", "annotations averaged per song", "dynamic (per second annotations)", "arousal.csv")
static_1_path = os.path.join(dataset_path, "DEAM_Annotations", "annotations", "annotations averaged per song", "song_level", "static_annotations_averaged_songs_1_2000.csv")
static_2_path = os.path.join(dataset_path, "DEAM_Annotations", "annotations", "annotations averaged per song", "song_level", "static_annotations_averaged_songs_2000_2058.csv")
In [11]:
# Load and show head for each
valence_df = pd.read_csv(valence_path)
arousal_df = pd.read_csv(arousal_path)
static_1_df = pd.read_csv(static_1_path)
static_2_df = pd.read_csv(static_2_path)
Valence¶
File contains second-by-second (time-series) average valence ratings for each song, indicating how emotionally positive or negative the music is over time. 0 is sad, 1 is happy.
In [12]:
print(" Valence (dynamic, per second):")
display(valence_df.head())
valence_df.describe()
Valence (dynamic, per second):
| song_id | sample_15000ms | sample_15500ms | sample_16000ms | sample_16500ms | sample_17000ms | sample_17500ms | sample_18000ms | sample_18500ms | sample_19000ms | ... | sample_621500ms | sample_622000ms | sample_622500ms | sample_623000ms | sample_623500ms | sample_624000ms | sample_624500ms | sample_625000ms | sample_625500ms | sample_626000ms | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2 | -0.073341 | -0.074661 | -0.074077 | -0.078154 | -0.081588 | -0.080873 | -0.083611 | -0.082145 | -0.081632 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | 3 | -0.189702 | -0.187765 | -0.183740 | -0.188761 | -0.193704 | -0.190834 | -0.189041 | -0.197554 | -0.202871 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | 4 | 0.154286 | 0.148063 | 0.146956 | 0.151180 | 0.135942 | 0.076832 | 0.084487 | 0.088513 | 0.090944 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | 5 | 0.149053 | 0.148260 | 0.147369 | 0.146956 | 0.147857 | 0.146927 | 0.145651 | 0.145795 | 0.146420 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 7 | 0.342816 | 0.345193 | 0.332023 | 0.328887 | 0.327603 | 0.329011 | 0.327883 | 0.334822 | 0.328447 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
5 rows × 1224 columns
Out[12]:
| song_id | sample_15000ms | sample_15500ms | sample_16000ms | sample_16500ms | sample_17000ms | sample_17500ms | sample_18000ms | sample_18500ms | sample_19000ms | ... | sample_621500ms | sample_622000ms | sample_622500ms | sample_623000ms | sample_623500ms | sample_624000ms | sample_624500ms | sample_625000ms | sample_625500ms | sample_626000ms | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | ... | 1.000 | 1.000 | 1.000 | 1.000 | 1.00 | 1.00 | 1.000 | 1.00 | 1.000 | 1.000 |
| mean | 1093.215871 | 0.100265 | 0.100442 | 0.100808 | 0.100297 | 0.100203 | 0.099390 | 0.099205 | 0.099683 | 0.099590 | ... | -0.158 | -0.158 | -0.156 | -0.154 | -0.15 | -0.15 | -0.152 | -0.15 | -0.136 | -0.112 |
| std | 599.658004 | 0.210479 | 0.211540 | 0.212936 | 0.213369 | 0.215074 | 0.216388 | 0.217358 | 0.218508 | 0.219200 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| min | 2.000000 | -0.581823 | -0.584132 | -0.593335 | -0.593696 | -0.595547 | -0.600594 | -0.611527 | -0.613262 | -0.615290 | ... | -0.158 | -0.158 | -0.156 | -0.154 | -0.15 | -0.15 | -0.152 | -0.15 | -0.136 | -0.112 |
| 25% | 569.500000 | -0.048420 | -0.045530 | -0.046436 | -0.047475 | -0.048000 | -0.048000 | -0.050986 | -0.049500 | -0.050868 | ... | -0.158 | -0.158 | -0.156 | -0.154 | -0.15 | -0.15 | -0.152 | -0.15 | -0.136 | -0.112 |
| 50% | 1157.500000 | 0.105500 | 0.105630 | 0.107000 | 0.105000 | 0.104594 | 0.103500 | 0.103448 | 0.100500 | 0.103000 | ... | -0.158 | -0.158 | -0.156 | -0.154 | -0.15 | -0.15 | -0.152 | -0.15 | -0.136 | -0.112 |
| 75% | 1607.750000 | 0.254341 | 0.255000 | 0.258975 | 0.259000 | 0.263948 | 0.261392 | 0.264000 | 0.267750 | 0.266049 | ... | -0.158 | -0.158 | -0.156 | -0.154 | -0.15 | -0.15 | -0.152 | -0.15 | -0.136 | -0.112 |
| max | 2058.000000 | 0.614000 | 0.601000 | 0.600000 | 0.614000 | 0.627000 | 0.641000 | 0.634000 | 0.611000 | 0.636000 | ... | -0.158 | -0.158 | -0.156 | -0.154 | -0.15 | -0.15 | -0.152 | -0.15 | -0.136 | -0.112 |
8 rows × 1224 columns
Arousal file¶
Contains second-by-second (time-series) average arousal ratings for each song, reflecting the intensity or energy level of the music over time. 0 is calm, 1 is ubpeat.
In [13]:
print("\n Arousal (dynamic, per second):")
display(arousal_df.head())
arousal_df.describe()
Arousal (dynamic, per second):
| song_id | sample_15000ms | sample_15500ms | sample_16000ms | sample_16500ms | sample_17000ms | sample_17500ms | sample_18000ms | sample_18500ms | sample_19000ms | ... | sample_622000ms | sample_622500ms | sample_623000ms | sample_623500ms | sample_624000ms | sample_624500ms | sample_625000ms | sample_625500ms | sample_626000ms | sample_626500ms | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2 | -0.109386 | -0.114942 | -0.116413 | -0.118613 | -0.126457 | -0.133199 | -0.136855 | -0.144713 | -0.138985 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | 3 | -0.110846 | -0.123973 | -0.131103 | -0.135956 | -0.140775 | -0.144664 | -0.163118 | -0.165218 | -0.158858 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | 4 | 0.222327 | 0.179446 | 0.178388 | 0.184056 | 0.176042 | 0.178720 | 0.176345 | 0.175793 | 0.176154 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | 5 | -0.255613 | -0.251579 | -0.251958 | -0.251124 | -0.250763 | -0.251957 | -0.251957 | -0.251957 | -0.251957 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | 7 | 0.464234 | 0.460789 | 0.460991 | 0.461046 | 0.457240 | 0.465702 | 0.471809 | 0.469918 | 0.473377 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
5 rows × 1225 columns
Out[13]:
| song_id | sample_15000ms | sample_15500ms | sample_16000ms | sample_16500ms | sample_17000ms | sample_17500ms | sample_18000ms | sample_18500ms | sample_19000ms | ... | sample_622000ms | sample_622500ms | sample_623000ms | sample_623500ms | sample_624000ms | sample_624500ms | sample_625000ms | sample_625500ms | sample_626000ms | sample_626500ms | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | 1802.000000 | ... | 1.00 | 1.00 | 1.000 | 1.00 | 1.000 | 1.000 | 1.00 | 1.000 | 1.00 | 1.000 |
| mean | 1093.215871 | 0.123961 | 0.124999 | 0.125694 | 0.126370 | 0.126515 | 0.126578 | 0.126825 | 0.127375 | 0.127409 | ... | -0.05 | -0.05 | -0.056 | -0.17 | -0.236 | -0.332 | -0.37 | -0.392 | -0.39 | -0.378 |
| std | 599.658004 | 0.266756 | 0.267682 | 0.268578 | 0.269261 | 0.269770 | 0.270248 | 0.270959 | 0.272065 | 0.272942 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| min | 2.000000 | -0.654305 | -0.654348 | -0.655628 | -0.663082 | -0.664821 | -0.665411 | -0.667466 | -0.668622 | -0.661271 | ... | -0.05 | -0.05 | -0.056 | -0.17 | -0.236 | -0.332 | -0.37 | -0.392 | -0.39 | -0.378 |
| 25% | 569.500000 | -0.071864 | -0.070000 | -0.072250 | -0.072093 | -0.073750 | -0.069962 | -0.076000 | -0.076922 | -0.077000 | ... | -0.05 | -0.05 | -0.056 | -0.17 | -0.236 | -0.332 | -0.37 | -0.392 | -0.39 | -0.378 |
| 50% | 1157.500000 | 0.139000 | 0.140830 | 0.144438 | 0.144235 | 0.143667 | 0.140169 | 0.141000 | 0.142247 | 0.142000 | ... | -0.05 | -0.05 | -0.056 | -0.17 | -0.236 | -0.332 | -0.37 | -0.392 | -0.39 | -0.378 |
| 75% | 1607.750000 | 0.341000 | 0.341581 | 0.343351 | 0.344000 | 0.343506 | 0.344971 | 0.346750 | 0.348424 | 0.350978 | ... | -0.05 | -0.05 | -0.056 | -0.17 | -0.236 | -0.332 | -0.37 | -0.392 | -0.39 | -0.378 |
| max | 2058.000000 | 0.678000 | 0.687000 | 0.688000 | 0.696000 | 0.693000 | 0.688000 | 0.695000 | 0.698000 | 0.702000 | ... | -0.05 | -0.05 | -0.056 | -0.17 | -0.236 | -0.332 | -0.37 | -0.392 | -0.39 | -0.378 |
8 rows × 1225 columns
static_annotations_averaged_songs_1_2000.csv¶
Provides overall mean and standard deviation values for valence and arousal for each of the first 2000 songs, summarizing their emotional content across time and raters.
In [14]:
print("\n Static Annotations (1–2000):")
display(static_1_df.head())
static_1_df.describe()
Static Annotations (1–2000):
| song_id | valence_mean | valence_std | arousal_mean | arousal_std | |
|---|---|---|---|---|---|
| 0 | 2 | 3.1 | 0.94 | 3.0 | 0.63 |
| 1 | 3 | 3.5 | 1.75 | 3.3 | 1.62 |
| 2 | 4 | 5.7 | 1.42 | 5.5 | 1.63 |
| 3 | 5 | 4.4 | 2.01 | 5.3 | 1.85 |
| 4 | 7 | 5.8 | 1.47 | 6.4 | 1.69 |
Out[14]:
| song_id | valence_mean | valence_std | arousal_mean | arousal_std | |
|---|---|---|---|---|---|
| count | 1744.000000 | 1744.000000 | 1744.000000 | 1744.000000 | 1744.000000 |
| mean | 1062.077982 | 4.903492 | 1.521950 | 4.812603 | 1.466778 |
| std | 584.299005 | 1.173920 | 0.385496 | 1.289368 | 0.367197 |
| min | 2.000000 | 1.600000 | 0.300000 | 1.600000 | 0.460000 |
| 25% | 536.750000 | 4.100000 | 1.270000 | 3.800000 | 1.200000 |
| 50% | 1128.500000 | 4.900000 | 1.510000 | 4.900000 | 1.480000 |
| 75% | 1564.250000 | 5.800000 | 1.780000 | 5.800000 | 1.720000 |
| max | 2000.000000 | 8.400000 | 2.900000 | 8.100000 | 2.590000 |
static_annotations_averaged_songs_2000_2058.csv¶
Provides extended emotional statistics—including mean, max, and min values with standard deviations—for valence and arousal for songs 2000 to 2058, offering a more detailed summary of their emotional dynamics.
In [15]:
print("\n Static Annotations (2000–2058):")
display(static_2_df.head())
static_2_df.describe()
Static Annotations (2000–2058):
| song_id | valence_mean | valence_std | valence_ max_mean | valence_max_std | valence_min_mean | valence_min_std | arousal_mean | arousal_std | arousal_max_mean | arousal_max_std | arousal_min_mean | arousal_min_std | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2001 | 3.2 | 0.98 | 5.0 | 1.41 | 2.2 | 0.98 | 6.6 | 0.80 | 8.6 | 0.49 | 3.4 | 1.02 |
| 1 | 2002 | 6.4 | 0.49 | 8.2 | 0.98 | 5.0 | 1.10 | 5.2 | 1.17 | 7.4 | 1.36 | 2.2 | 1.17 |
| 2 | 2003 | 5.4 | 1.50 | 7.2 | 1.17 | 3.4 | 1.02 | 4.6 | 1.85 | 6.2 | 2.04 | 1.4 | 0.49 |
| 3 | 2004 | 5.0 | 1.10 | 6.4 | 1.02 | 3.2 | 1.17 | 4.8 | 1.60 | 6.0 | 2.28 | 2.8 | 0.98 |
| 4 | 2005 | 3.8 | 1.17 | 5.0 | 1.10 | 1.6 | 0.80 | 5.2 | 0.75 | 8.4 | 0.80 | 2.0 | 1.10 |
Out[15]:
| song_id | valence_mean | valence_std | valence_ max_mean | valence_max_std | valence_min_mean | valence_min_std | arousal_mean | arousal_std | arousal_max_mean | arousal_max_std | arousal_min_mean | arousal_min_std | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 58.000000 | 58.000000 | 58.000000 | 58.000000 | 58.000000 | 58.000000 | 58.000000 | 58.000000 | 58.000000 | 58.000000 | 58.000000 | 58.000000 | 58.000000 |
| mean | 2029.500000 | 4.923793 | 0.913448 | 5.918966 | 0.992586 | 3.864138 | 0.935000 | 4.856897 | 1.340690 | 6.116724 | 1.414310 | 3.305172 | 1.283103 |
| std | 16.886879 | 1.193966 | 0.388748 | 1.096843 | 0.362838 | 1.266040 | 0.330726 | 1.051779 | 0.449697 | 1.266050 | 0.531688 | 0.945087 | 0.358815 |
| min | 2001.000000 | 3.000000 | 0.370000 | 4.200000 | 0.400000 | 1.600000 | 0.370000 | 3.000000 | 0.370000 | 4.000000 | 0.470000 | 1.400000 | 0.490000 |
| 25% | 2015.250000 | 3.807500 | 0.492500 | 5.000000 | 0.750000 | 2.800000 | 0.750000 | 4.250000 | 1.117500 | 5.200000 | 1.020000 | 2.600000 | 1.020000 |
| 50% | 2029.500000 | 5.000000 | 0.925000 | 6.000000 | 0.980000 | 3.900000 | 0.920000 | 4.635000 | 1.360000 | 6.100000 | 1.465000 | 3.085000 | 1.260000 |
| 75% | 2043.750000 | 5.950000 | 1.170000 | 6.822500 | 1.200000 | 4.900000 | 1.170000 | 5.400000 | 1.615000 | 7.197500 | 1.790000 | 3.950000 | 1.522500 |
| max | 2058.000000 | 7.200000 | 1.850000 | 8.200000 | 1.790000 | 6.400000 | 1.670000 | 7.000000 | 2.330000 | 8.600000 | 2.580000 | 5.800000 | 2.150000 |
The remaining data are mp3 files and csv's with one-rater-per-song data (from which the above csv's were compiled).¶
In [16]:
valence_df = pd.read_csv(valence_path, index_col=0)
arousal_df = pd.read_csv(arousal_path, index_col=0)
static_df = pd.read_csv(static_1_path)
static_df.columns = static_df.columns.str.strip()
In [17]:
print("Original valence range:", static_df['valence_mean'].min(), "to", static_df['valence_mean'].max())
print("Original arousal range:", static_df['arousal_mean'].min(), "to", static_df['arousal_mean'].max())
Original valence range: 1.6 to 8.4 Original arousal range: 1.6 to 8.1
In [18]:
# Min-max normalize valence and arousal
static_df['valence_mean'] = static_df['valence_mean'].clip(0, 9) / 9.0
static_df['arousal_mean'] = static_df['arousal_mean'].clip(0, 9) / 9.0
In [19]:
print("Normalized valence range:", static_df['valence_mean'].min(), "to", static_df['valence_mean'].max())
print("Normalized arousal range:", static_df['arousal_mean'].min(), "to", static_df['arousal_mean'].max())
Normalized valence range: 0.17777777777777778 to 0.9333333333333333 Normalized arousal range: 0.17777777777777778 to 0.8999999999999999
In [20]:
def preprocess_static_songs(mp3_files,
static_df,
sr=22050,
n_mels=128,
hop_length=256,
duration_sec=30,
top_db=80):
X_real, conditions = [], []
target_frames = int(np.ceil((sr * duration_sec) / hop_length))
for mp3 in tqdm(mp3_files):
song_id = int(os.path.splitext(os.path.basename(mp3))[0])
if song_id not in static_df.index:
continue
try:
valence = static_df.loc[song_id, 'valence_mean']
arousal = static_df.loc[song_id, 'arousal_mean']
except KeyError:
continue
try:
y, _ = librosa.load(mp3, sr=sr, duration=duration_sec)
except Exception as e:
print(f"Failed to load {mp3}: {e}")
continue
mel = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=n_mels, hop_length=hop_length)
mel_db = librosa.power_to_db(mel, ref=np.max, top_db=top_db)
mel_norm = np.clip((mel_db + top_db) / top_db, 0, 1)
if mel_norm.shape[1] < target_frames:
pad = target_frames - mel_norm.shape[1]
mel_norm = np.pad(mel_norm, ((0, 0), (0, pad)), mode='constant')
else:
mel_norm = mel_norm[:, :target_frames]
X_real.append(mel_norm[..., np.newaxis])
conditions.append([valence, arousal])
X_real = np.array(X_real, dtype=np.float32)
X_real = (X_real * 2) - 1 # scale to [-1, 1]
conditions = np.array(conditions, dtype=np.float32)
return X_real, conditions
In [21]:
static_df.index = static_df.index.astype(int)
valid_ids = set(static_df.index)
valid_mp3_files = [f for f in mp3_files
if int(os.path.splitext(os.path.basename(f))[0]) in valid_ids]
ordered_ids = sorted(valid_ids)
ordered_mp3_files = []
for sid in ordered_ids:
path = os.path.join(audio_dir, f"{sid}.mp3")
if os.path.exists(path):
ordered_mp3_files.append(path)
print(f"Found {len(ordered_mp3_files)} valid files")
from tqdm import tqdm
import numpy as np
X_real, conditions = preprocess_static_songs(ordered_mp3_files, static_df)
Found 1487 valid files
100%|██████████| 1487/1487 [04:07<00:00, 6.00it/s]
In [22]:
#trim spectrograms to (128, 2560, 1)
X_real = X_real[:, :, :2560, :]
In [23]:
print(X_real.shape)
print(conditions.shape)
(1487, 128, 2560, 1) (1487, 2)
Verify that our "conditions" data match the original CSV valence and arousal means¶
In [24]:
import random
import os
example_index = random.randint(0, len(ordered_mp3_files) - 1)
example_file = ordered_mp3_files[example_index]
filename = os.path.splitext(os.path.basename(example_file))[0]
song_id = int(filename)
print(f"Checking song_id: {song_id}")
v_csv = static_df.loc[song_id, 'valence_mean']
a_csv = static_df.loc[song_id, 'arousal_mean']
v_extracted = conditions[example_index, 0]
a_extracted = conditions[example_index, 1]
print(f"Valence → CSV: {v_csv:.4f} | Extracted: {v_extracted:.4f}")
print(f"Arousal → CSV: {a_csv:.4f} | Extracted: {a_extracted:.4f}")
Checking song_id: 1575 Valence → CSV: 0.6333 | Extracted: 0.6333 Arousal → CSV: 0.5778 | Extracted: 0.5778
CVAE Training Below (Conditional VAE) with annealing to support
In [25]:
import tensorflow as tf
from tensorflow.keras import layers, Model, Input
from tensorflow.keras.losses import MeanSquaredError
from tensorflow.keras.optimizers import Adam
latent_dim = 256
mel_shape = (128, 2560, 1)
cond_dim = 2
def build_encoder():
mel_input = Input(shape=mel_shape)
cond_input = Input(shape=(cond_dim,))
x = layers.Conv2D(32, 4, strides=2, padding="same", activation="relu")(mel_input)
x = layers.Conv2D(64, 4, strides=2, padding="same", activation="relu")(x)
x = layers.Conv2D(128, 4, strides=2, padding="same", activation="relu")(x)
x = layers.Conv2D(256, 4, strides=2, padding="same", activation="relu")(x)
x = layers.Conv2D(512, 4, strides=2, padding="same", activation="relu")(x)
x = layers.Flatten()(x)
x = layers.Concatenate()([x, cond_input])
x = layers.Dense(512, activation="relu")(x)
z_mean = layers.Dense(latent_dim)(x)
z_log_var = layers.Dense(latent_dim)(x)
def sampling(args):
z_mean, z_log_var = args
epsilon = tf.random.normal(shape=tf.shape(z_mean))
return z_mean + tf.exp(0.5 * z_log_var) * epsilon
z = layers.Lambda(sampling)([z_mean, z_log_var])
return Model([mel_input, cond_input], [z_mean, z_log_var, z], name="encoder")
def build_decoder():
z_input = Input(shape=(latent_dim,))
cond_input = Input(shape=(cond_dim,))
x = layers.Concatenate()([z_input, cond_input])
x = layers.Dense(4 * 80 * 512, activation="relu")(x)
x = layers.Reshape((4, 80, 512))(x)
x = layers.Conv2DTranspose(256, 4, strides=2, padding="same", activation="relu")(x)
x = layers.Conv2DTranspose(128, 4, strides=2, padding="same", activation="relu")(x)
x = layers.Conv2DTranspose(64, 4, strides=2, padding="same", activation="relu")(x)
x = layers.Conv2DTranspose(32, 4, strides=2, padding="same", activation="relu")(x)
x = layers.Conv2DTranspose(16, 4, strides=2, padding="same", activation="relu")(x)
x = layers.Conv2DTranspose(1, 3, padding="same", activation="tanh")(x)
return Model([z_input, cond_input], x, name="decoder")
class CVAE(tf.keras.Model):
def __init__(self, encoder, decoder, kl_annealing_epochs=20, **kwargs):
super(CVAE, self).__init__(**kwargs)
self.encoder = encoder
self.decoder = decoder
self.kl_annealing_epochs = kl_annealing_epochs
self.total_loss_tracker = tf.keras.metrics.Mean(name="total_loss")
self.recon_loss_tracker = tf.keras.metrics.Mean(name="recon_loss")
self.kl_loss_tracker = tf.keras.metrics.Mean(name="kl_loss")
self.recon_loss_fn = MeanSquaredError()
def compile(self, optimizer):
super().compile()
self.optimizer = optimizer
def train_step(self, data):
(x_inputs, y_true) = data
x, c = x_inputs
step = tf.cast(self.optimizer.iterations, tf.float32)
kl_weight = tf.minimum(1.0, step / (self.kl_annealing_epochs * 1000.0))
with tf.GradientTape() as tape:
z_mean, z_log_var, z = self.encoder([x, c])
x_recon = self.decoder([z, c])
recon_loss = self.recon_loss_fn(y_true, x_recon)
kl_loss = -0.5 * tf.reduce_mean(1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))
total_loss = recon_loss + kl_weight * kl_loss
grads = tape.gradient(total_loss, self.trainable_weights)
self.optimizer.apply_gradients(zip(grads, self.trainable_weights))
self.total_loss_tracker.update_state(total_loss)
self.recon_loss_tracker.update_state(recon_loss)
self.kl_loss_tracker.update_state(kl_loss)
return {
"loss": self.total_loss_tracker.result(),
"recon_loss": self.recon_loss_tracker.result(),
"kl_loss": self.kl_loss_tracker.result(),
"kl_weight": kl_weight,
}
In [26]:
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
encoder = build_encoder()
decoder = build_decoder()
cvae = CVAE(encoder, decoder, kl_annealing_epochs=20)
cvae.compile(optimizer=Adam(learning_rate=1e-4))
early_stop_cb = tf.keras.callbacks.EarlyStopping(
monitor="loss", patience=15, restore_best_weights=True
)
cvae.fit(
x=(X_real, conditions),
y=X_real,
epochs=100,
batch_size=32,
callbacks=[early_stop_cb]
)
Epoch 1/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 44s 359ms/step - kl_loss: 0.1074 - kl_weight: 0.0012 - loss: 0.1987 - recon_loss: 0.1985 Epoch 2/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 1.0719 - kl_weight: 0.0035 - loss: 0.1022 - recon_loss: 0.0993 Epoch 3/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.3790 - kl_weight: 0.0059 - loss: 0.0882 - recon_loss: 0.0863 Epoch 4/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.2237 - kl_weight: 0.0082 - loss: 0.0831 - recon_loss: 0.0813 Epoch 5/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.1964 - kl_weight: 0.0106 - loss: 0.0811 - recon_loss: 0.0791 Epoch 6/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.1686 - kl_weight: 0.0129 - loss: 0.0788 - recon_loss: 0.0768 Epoch 7/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.1544 - kl_weight: 0.0153 - loss: 0.0775 - recon_loss: 0.0752 Epoch 8/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.1597 - kl_weight: 0.0176 - loss: 0.0759 - recon_loss: 0.0732 Epoch 9/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.1519 - kl_weight: 0.0200 - loss: 0.0709 - recon_loss: 0.0680 Epoch 10/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 78ms/step - kl_loss: 0.1471 - kl_weight: 0.0223 - loss: 0.0667 - recon_loss: 0.0635 Epoch 11/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.1359 - kl_weight: 0.0247 - loss: 0.0623 - recon_loss: 0.0590 Epoch 12/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 78ms/step - kl_loss: 0.1540 - kl_weight: 0.0270 - loss: 0.0619 - recon_loss: 0.0578 Epoch 13/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.1387 - kl_weight: 0.0294 - loss: 0.0599 - recon_loss: 0.0559 Epoch 14/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 78ms/step - kl_loss: 0.1308 - kl_weight: 0.0317 - loss: 0.0596 - recon_loss: 0.0555 Epoch 15/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 78ms/step - kl_loss: 0.1251 - kl_weight: 0.0341 - loss: 0.0596 - recon_loss: 0.0554 Epoch 16/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.1171 - kl_weight: 0.0364 - loss: 0.0589 - recon_loss: 0.0547 Epoch 17/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.1120 - kl_weight: 0.0388 - loss: 0.0589 - recon_loss: 0.0547 Epoch 18/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 78ms/step - kl_loss: 0.1086 - kl_weight: 0.0411 - loss: 0.0583 - recon_loss: 0.0539 Epoch 19/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 78ms/step - kl_loss: 0.1019 - kl_weight: 0.0435 - loss: 0.0593 - recon_loss: 0.0549 Epoch 20/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.1009 - kl_weight: 0.0458 - loss: 0.0585 - recon_loss: 0.0540 Epoch 21/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 78ms/step - kl_loss: 0.0971 - kl_weight: 0.0482 - loss: 0.0595 - recon_loss: 0.0548 Epoch 22/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0966 - kl_weight: 0.0505 - loss: 0.0587 - recon_loss: 0.0538 Epoch 23/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0932 - kl_weight: 0.0529 - loss: 0.0585 - recon_loss: 0.0537 Epoch 24/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.0882 - kl_weight: 0.0552 - loss: 0.0583 - recon_loss: 0.0535 Epoch 25/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0847 - kl_weight: 0.0576 - loss: 0.0584 - recon_loss: 0.0536 Epoch 26/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 78ms/step - kl_loss: 0.0837 - kl_weight: 0.0599 - loss: 0.0581 - recon_loss: 0.0532 Epoch 27/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.0771 - kl_weight: 0.0623 - loss: 0.0574 - recon_loss: 0.0527 Epoch 28/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0761 - kl_weight: 0.0646 - loss: 0.0574 - recon_loss: 0.0525 Epoch 29/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0740 - kl_weight: 0.0670 - loss: 0.0581 - recon_loss: 0.0532 Epoch 30/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0732 - kl_weight: 0.0693 - loss: 0.0570 - recon_loss: 0.0520 Epoch 31/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.0707 - kl_weight: 0.0717 - loss: 0.0575 - recon_loss: 0.0525 Epoch 32/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.0662 - kl_weight: 0.0740 - loss: 0.0566 - recon_loss: 0.0518 Epoch 33/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0650 - kl_weight: 0.0764 - loss: 0.0570 - recon_loss: 0.0521 Epoch 34/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 78ms/step - kl_loss: 0.0626 - kl_weight: 0.0787 - loss: 0.0570 - recon_loss: 0.0521 Epoch 35/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0638 - kl_weight: 0.0811 - loss: 0.0573 - recon_loss: 0.0521 Epoch 36/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 73ms/step - kl_loss: 0.0609 - kl_weight: 0.0834 - loss: 0.0567 - recon_loss: 0.0517 Epoch 37/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 73ms/step - kl_loss: 0.0592 - kl_weight: 0.0858 - loss: 0.0582 - recon_loss: 0.0532 Epoch 38/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 79ms/step - kl_loss: 0.0593 - kl_weight: 0.0881 - loss: 0.0574 - recon_loss: 0.0522 Epoch 39/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0584 - kl_weight: 0.0905 - loss: 0.0578 - recon_loss: 0.0526 Epoch 40/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0565 - kl_weight: 0.0928 - loss: 0.0574 - recon_loss: 0.0522 Epoch 41/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 4s 74ms/step - kl_loss: 0.0549 - kl_weight: 0.0952 - loss: 0.0572 - recon_loss: 0.0520 Epoch 42/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0525 - kl_weight: 0.0975 - loss: 0.0576 - recon_loss: 0.0525 Epoch 43/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0499 - kl_weight: 0.0999 - loss: 0.0565 - recon_loss: 0.0515 Epoch 44/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 73ms/step - kl_loss: 0.0505 - kl_weight: 0.1022 - loss: 0.0583 - recon_loss: 0.0531 Epoch 45/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0489 - kl_weight: 0.1046 - loss: 0.0577 - recon_loss: 0.0526 Epoch 46/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0506 - kl_weight: 0.1069 - loss: 0.0572 - recon_loss: 0.0518 Epoch 47/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0483 - kl_weight: 0.1093 - loss: 0.0579 - recon_loss: 0.0526 Epoch 48/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0467 - kl_weight: 0.1116 - loss: 0.0566 - recon_loss: 0.0514 Epoch 49/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0481 - kl_weight: 0.1140 - loss: 0.0575 - recon_loss: 0.0521 Epoch 50/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 73ms/step - kl_loss: 0.0453 - kl_weight: 0.1163 - loss: 0.0572 - recon_loss: 0.0520 Epoch 51/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0457 - kl_weight: 0.1187 - loss: 0.0573 - recon_loss: 0.0519 Epoch 52/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 73ms/step - kl_loss: 0.0454 - kl_weight: 0.1210 - loss: 0.0572 - recon_loss: 0.0517 Epoch 53/100 47/47 ━━━━━━━━━━━━━━━━━━━━ 3s 72ms/step - kl_loss: 0.0449 - kl_weight: 0.1234 - loss: 0.0574 - recon_loss: 0.0519
Out[26]:
<keras.src.callbacks.history.History at 0x7d985dd8ae90>
In [27]:
import numpy as np
import librosa
import librosa.display
import matplotlib.pyplot as plt
condition = np.array([[0.9, 0.8]], dtype=np.float32)
z = tf.random.normal((1, latent_dim))
generated = decoder.predict([z, condition])
mel = generated[0, :, :, 0]
mel_db = ((mel + 1) / 2) * 80 - 80
plt.figure(figsize=(12, 4))
librosa.display.specshow(mel_db, sr=22050, hop_length=256, x_axis='time', y_axis='mel')
plt.colorbar(format="%+2.0f dB")
plt.title("Generated 30-Second Mel Spectrogram")
plt.tight_layout()
plt.show()
1/1 ━━━━━━━━━━━━━━━━━━━━ 1s 840ms/step
Pick a similarly labeled spectrogram from the data to compare next to our generated spectrogram
In [28]:
import numpy as np
import librosa.display
import matplotlib.pyplot as plt
target_condition = np.array([0.9, 0.8], dtype=np.float32)
distances = np.linalg.norm(conditions - target_condition, axis=1)
closest_index = np.argmin(distances)
print(f"Closest match index: {closest_index}, condition: {conditions[closest_index]}")
mel_real = X_real[closest_index, :, :, 0]
mel_real_db = ((mel_real + 1) / 2) * 80 - 80
mel_generated_db = ((mel + 1) / 2) * 80 - 80
fig, axs = plt.subplots(1, 2, figsize=(14, 4))
img1 = librosa.display.specshow(
mel_real_db, sr=22050, hop_length=256,
y_axis='mel', x_axis='time', ax=axs[0]
)
axs[0].set_title(f"Closest Real Spectrogram\nValence: {conditions[closest_index][0]:.2f}, Arousal: {conditions[closest_index][1]:.2f}")
fig.colorbar(img1, ax=axs[0], format="%+2.0f dB")
img2 = librosa.display.specshow(
mel_generated_db, sr=22050, hop_length=256,
y_axis='mel', x_axis='time', ax=axs[1]
)
axs[1].set_title("Generated Spectrogram\nCondition: [0.9, 0.8]")
fig.colorbar(img2, ax=axs[1], format="%+2.0f dB")
plt.tight_layout()
plt.show()
Closest match index: 1247, condition: [0.87777776 0.7888889 ]
In [29]:
from google.colab import files
np.save("generated_mel.npy", mel)
print("Saved as generated_mel.npy")
files.download("generated_mel.npy")
Saved as generated_mel.npy
Below is GAN training to compare performance
In [30]:
import os
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers, Model, Input
from tensorflow.keras.optimizers import Adam
latent_dim = 256
mel_shape = (128, 2560, 1)
cond_dim = 2
In [31]:
def build_generator():
z_input = Input(shape=(latent_dim,))
c_input = Input(shape=(cond_dim,))
x = layers.Concatenate()([z_input, c_input])
x = layers.Dense(8 * 160 * 1024, activation="relu")(x)
x = layers.Reshape((8, 160, 1024))(x)
x = layers.Conv2DTranspose(512, 4, strides=2, padding='same')(x)
x = layers.BatchNormalization()(x)
x = layers.Dropout(0.3)(x)
x = layers.ReLU()(x)
x = layers.Conv2DTranspose(256, 4, strides=2, padding='same')(x)
x = layers.BatchNormalization()(x)
x = layers.Dropout(0.3)(x)
x = layers.ReLU()(x)
x = layers.Conv2DTranspose(128, 4, strides=2, padding='same')(x)
x = layers.BatchNormalization()(x)
x = layers.Dropout(0.3)(x)
x = layers.ReLU()(x)
x = layers.Conv2DTranspose(64, 4, strides=2, padding='same')(x)
x = layers.BatchNormalization()(x)
x = layers.Dropout(0.3)(x)
x = layers.ReLU()(x)
x = layers.Conv2DTranspose(1, 3, padding='same', activation='tanh')(x)
return Model([z_input, c_input], x, name="generator")
In [32]:
def build_discriminator():
mel_input = Input(shape=mel_shape)
c_input = Input(shape=(cond_dim,))
x = layers.Conv2D(64, 4, strides=2, padding='same')(mel_input)
x = layers.LeakyReLU(negative_slope=0.2)(x)
x = layers.Dropout(0.3)(x)
x = layers.Conv2D(128, 4, strides=2, padding='same')(x)
x = layers.LeakyReLU(negative_slope=0.2)(x)
x = layers.Dropout(0.3)(x)
# x = layers.Conv2D(256, 4, strides=2, padding='same')(x)
# x = layers.LeakyReLU(negative_slope=0.2)(x)
# x = layers.Dropout(0.3)(x)
x = layers.Flatten()(x)
x = layers.Concatenate()([x, c_input])
x = layers.Dense(1)(x)
return Model([mel_input, c_input], x, name="discriminator")
In [33]:
generator = build_generator()
discriminator = build_discriminator()
discriminator.compile(optimizer=Adam(1e-4), loss=tf.keras.losses.BinaryCrossentropy(from_logits=True))
z_input = Input(shape=(latent_dim,))
c_input = Input(shape=(cond_dim,))
fake_mel = generator([z_input, c_input])
validity = discriminator([fake_mel, c_input])
gan = Model([z_input, c_input], validity)
gan.compile(optimizer=Adam(1e-5), loss=tf.keras.losses.BinaryCrossentropy(from_logits=True))
In [34]:
def train_gan(X_real, C_real, epochs=50, batch_size=8):
steps_per_epoch = X_real.shape[0] // batch_size
for epoch in range(epochs):
d_losses, g_losses = [], []
for _ in range(steps_per_epoch):
idx = np.random.randint(0, X_real.shape[0], batch_size)
real_imgs = X_real[idx]
real_cond = C_real[idx]
z_noise = np.random.normal(0, 1, (batch_size, latent_dim))
fake_imgs = generator([z_noise, real_cond], training=False)
#real_labels = np.ones((batch_size, 1)) * 0.9
#fake_labels = np.zeros((batch_size, 1))
real_labels = np.ones((batch_size, 1)) * np.random.uniform(0.7, 1.0)
fake_labels = np.zeros((batch_size, 1)) + np.random.uniform(0.0, 0.3)
real_imgs += np.random.normal(0, 0.05, size=real_imgs.shape)
fake_imgs += np.random.normal(0, 0.05, size=fake_imgs.shape)
discriminator.trainable = True
d_loss_real = discriminator.train_on_batch([real_imgs, real_cond], real_labels)
d_loss_fake = discriminator.train_on_batch([fake_imgs, real_cond], fake_labels)
d_loss = 0.5 * (d_loss_real + d_loss_fake)
d_losses.append(d_loss)
discriminator.trainable = False
#train generator more than once
for _ in range(4):
z_noise = np.random.normal(0, 1, (batch_size, latent_dim))
g_loss = gan.train_on_batch([z_noise, real_cond], np.ones((batch_size, 1)))
g_losses.append(g_loss)
# z_noise = np.random.normal(0, 1, (batch_size, latent_dim))
# g_loss = gan.train_on_batch([z_noise, real_cond], np.ones((batch_size, 1)))
# g_losses.append(g_loss)
print(f"Epoch {epoch+1}/{epochs} - D: {np.mean(d_losses):.4f}, G: {np.mean(g_losses):.4f}")
This GAN was very hard to train. Getting the discriminator and generator to train without one dominating the other was extremely challenging. Solutions to this issue included the addition of dropout layers, noisy fake and real labels, and allowing the generator to train four times the amount of the discriminator
In [35]:
train_gan(
X_real=X_real,
C_real=conditions,
epochs=50,
batch_size=8
)
Epoch 1/50 - D: 0.5335, G: 1.7484 Epoch 2/50 - D: 0.6993, G: 2.2834 Epoch 3/50 - D: 0.6570, G: 1.7935 Epoch 4/50 - D: 0.6247, G: 1.5003 Epoch 5/50 - D: 0.6042, G: 1.3148 Epoch 6/50 - D: 0.5922, G: 1.1669 Epoch 7/50 - D: 0.5821, G: 1.0582 Epoch 8/50 - D: 0.5706, G: 0.9551 Epoch 9/50 - D: 0.5622, G: 0.8908 Epoch 10/50 - D: 0.5549, G: 0.8699 Epoch 11/50 - D: 0.5495, G: 0.8245 Epoch 12/50 - D: 0.5476, G: 0.7815 Epoch 13/50 - D: 0.5452, G: 0.7430 Epoch 14/50 - D: 0.5439, G: 0.7231 Epoch 15/50 - D: 0.5416, G: 0.7000 Epoch 16/50 - D: 0.5393, G: 0.6775 Epoch 17/50 - D: 0.5377, G: 0.6643 Epoch 18/50 - D: 0.5371, G: 0.6574 Epoch 19/50 - D: 0.5349, G: 0.6773 Epoch 20/50 - D: 0.5331, G: 0.6658 Epoch 21/50 - D: 0.5327, G: 0.6570 Epoch 22/50 - D: 0.5312, G: 0.6507 Epoch 23/50 - D: 0.5327, G: 0.6399 Epoch 24/50 - D: 0.5324, G: 0.6353 Epoch 25/50 - D: 0.5317, G: 0.6212 Epoch 26/50 - D: 0.5321, G: 0.6083 Epoch 27/50 - D: 0.5308, G: 0.5953 Epoch 28/50 - D: 0.5298, G: 0.5921 Epoch 29/50 - D: 0.5308, G: 0.5843 Epoch 30/50 - D: 0.5306, G: 0.5723 Epoch 31/50 - D: 0.5315, G: 0.5646 Epoch 32/50 - D: 0.5326, G: 0.5588 Epoch 33/50 - D: 0.5324, G: 0.5469 Epoch 34/50 - D: 0.5323, G: 0.5350 Epoch 35/50 - D: 0.5324, G: 0.5253 Epoch 36/50 - D: 0.5322, G: 0.5182 Epoch 37/50 - D: 0.5326, G: 0.5094 Epoch 38/50 - D: 0.5341, G: 0.5054 Epoch 39/50 - D: 0.5346, G: 0.5023 Epoch 40/50 - D: 0.5355, G: 0.4972 Epoch 41/50 - D: 0.5366, G: 0.4910 Epoch 42/50 - D: 0.5367, G: 0.4838 Epoch 43/50 - D: 0.5370, G: 0.4779 Epoch 44/50 - D: 0.5377, G: 0.4728 Epoch 45/50 - D: 0.5377, G: 0.4665 Epoch 46/50 - D: 0.5380, G: 0.4609 Epoch 47/50 - D: 0.5382, G: 0.4548 Epoch 48/50 - D: 0.5385, G: 0.4498 Epoch 49/50 - D: 0.5383, G: 0.4433 Epoch 50/50 - D: 0.5388, G: 0.4378
In [36]:
import numpy as np
import librosa
import librosa.display
import matplotlib.pyplot as plt
import tensorflow as tf
def compare_gan_generated_vs_real(generator, latent_dim, condition_vec, X_real, conditions, sr=22050, hop_length=256):
condition = np.array([condition_vec], dtype=np.float32)
z = tf.random.normal((1, latent_dim))
generated = generator.predict([z, condition], verbose=0)
mel_generated = generated[0, :, :, 0]
mel_generated_db = ((mel_generated + 1) / 2) * 80 - 80
distances = np.linalg.norm(conditions - condition_vec, axis=1)
closest_index = np.argmin(distances)
mel_real = X_real[closest_index, :, :, 0]
mel_real_db = ((mel_real + 1) / 2) * 80 - 80
print(f"Closest real match: valence={conditions[closest_index][0]:.2f}, arousal={conditions[closest_index][1]:.2f}")
fig, axs = plt.subplots(1, 2, figsize=(14, 4))
img1 = librosa.display.specshow(mel_real_db, sr=sr, hop_length=hop_length, y_axis='mel', x_axis='time', ax=axs[0])
axs[0].set_title(f"Closest Real\nValence: {conditions[closest_index][0]:.2f}, Arousal: {conditions[closest_index][1]:.2f}")
fig.colorbar(img1, ax=axs[0], format="%+2.0f dB")
img2 = librosa.display.specshow(mel_generated_db, sr=sr, hop_length=hop_length, y_axis='mel', x_axis='time', ax=axs[1])
axs[1].set_title(f"GAN Generated\nCondition: {condition_vec}")
fig.colorbar(img2, ax=axs[1], format="%+2.0f dB")
plt.tight_layout()
plt.show()
return mel_generated
In [37]:
compare_gan_generated_vs_real(
generator=generator,
latent_dim=latent_dim,
condition_vec=[0.9, 0.8],
X_real=X_real,
conditions=conditions
)
Closest real match: valence=0.88, arousal=0.79
Out[37]:
array([[-0.68342686, -0.86479586, -0.8356621 , ..., 0.4891419 ,
0.46206364, 0.40334255],
[-0.7780113 , -0.9267138 , -0.7723679 , ..., 0.72881883,
0.8155295 , 0.6267297 ],
[-0.95561033, -0.98444855, -0.9671945 , ..., 0.81777465,
0.80068177, 0.71370035],
...,
[-0.9002526 , -0.98374516, -0.97520137, ..., -0.9998013 ,
-0.99943817, -0.9993618 ],
[-0.8620061 , -0.9429547 , -0.9639903 , ..., -0.99902403,
-0.9991534 , -0.9977514 ],
[-0.8294475 , -0.9581124 , -0.97700894, ..., -0.9997613 ,
-0.9988584 , -0.98979384]], dtype=float32)
In [42]:
import librosa
import numpy as np
from IPython.display import Audio
def mel_to_audio_griffin_lim(mel_generated, sr=22050, n_fft=1024, hop_length=256, top_db=80):
mel_norm = (mel_generated + 1) / 2
mel_db = mel_norm * top_db - top_db
mel_power = librosa.db_to_power(mel_db)
S = librosa.feature.inverse.mel_to_stft(mel_power, sr=sr, n_fft=n_fft)
audio = librosa.griffinlim(S, n_iter=64, hop_length=hop_length, win_length=n_fft)
return audio
In [44]:
import soundfile as sf
mel1 = compare_gan_generated_vs_real(generator, latent_dim, [0.9, 0.8], X_real, conditions)
np.save("gan_generated_mel1.npy", mel1)
files.download("gan_generated_mel1.npy")
audio = mel_to_audio_griffin_lim(mel1)
Audio(audio, rate=22050)
sf.write("gan_generated_audio1.wav", audio, 22050)
files.download("gan_generated_audio1.wav")
Closest real match: valence=0.88, arousal=0.79
In [45]:
mel2 = compare_gan_generated_vs_real(generator, latent_dim, [0.5, 0.5], X_real, conditions)
np.save("gan_generated_mel2.npy", mel2)
files.download("gan_generated_mel2.npy")
audio = mel_to_audio_griffin_lim(mel2)
Audio(audio, rate=22050)
sf.write("gan_generated_audio2.wav", audio, 22050)
files.download("gan_generated_audio2.wav")
Closest real match: valence=0.50, arousal=0.50
In [46]:
mel3 = compare_gan_generated_vs_real(generator, latent_dim, [0.2, 0.2], X_real, conditions)
np.save("gan_generated_mel3.npy", mel3)
files.download("gan_generated_mel3.npy")
audio = mel_to_audio_griffin_lim(mel3)
Audio(audio, rate=22050)
sf.write("gan_generated_audio3.wav", audio, 22050)
files.download("gan_generated_audio3.wav")
Closest real match: valence=0.26, arousal=0.28
In [47]:
mel4 = compare_gan_generated_vs_real(generator, latent_dim, [0.8, 0.2], X_real, conditions)
np.save("gan_generated_mel4.npy", mel4)
files.download("gan_generated_mel4.npy")
audio = mel_to_audio_griffin_lim(mel4)
Audio(audio, rate=22050)
sf.write("gan_generated_audio4.wav", audio, 22050)
files.download("gan_generated_audio4.wav")
Closest real match: valence=0.73, arousal=0.28
In [48]:
mel5 = compare_gan_generated_vs_real(generator, latent_dim, [0.2, 0.8], X_real, conditions)
np.save("gan_generated_mel5.npy", mel5)
files.download("gan_generated_mel5.npy")
audio = mel_to_audio_griffin_lim(mel5)
Audio(audio, rate=22050)
sf.write("gan_generated_audio5.wav", audio, 22050)
files.download("gan_generated_audio5.wav")
Closest real match: valence=0.36, arousal=0.77
